Papers by Sungjin Han
Learning Slice-Aware Representations with Mixture of Attentions (2021.findings-acl)
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| Challenge: | Real-world machine learning systems are achieving excellent performance in terms of coarse-grained metrics like overall accuracy and F-1 score. |
| Approach: | They extend slice-based learning (SBL) with a mixture of attentions to learn slice-aware dual attentive representations. |
| Outcome: | The proposed approach outperforms the baseline method and the original SBL approach on monitored slices with two natural language understanding tasks. |
A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems (2021.emnlp-main)
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Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya
| Challenge: | Existing methods to improve NLU are laborintensive and expensive. |
| Approach: | They propose a scalable and automatic approach to improving NLU in a large-scale conversational AI system by leveraging implicit user feedback. |
| Outcome: | The proposed framework improves NLU in a large-scale conversational AI system across 10 domains. |
See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval (2026.findings-eacl)
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| Challenge: | Existing video moment retrieval methods rely on sparse frame sampling, risking information loss. |
| Approach: | a new video-based framework enhances memory efficiency while maintaining high information resolution . SMORE uses query-guided captions to encode semantics aligned with user intent . |
| Outcome: | a new framework improves memory efficiency while maintaining high information resolution . it achieves state-of-the-art performance on QVHighlights, Charades-STA, and ActivityNet-Captions benchmarks . |